{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 6: Synaptic Plasticity\n",
    "\n",
    "**Duration:** ~40 minutes | **Prerequisites:** Basic Tutorials, Tutorial 5\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "By the end of this tutorial, you will:\n",
    "\n",
    "- ✅ Understand short-term plasticity (STP) mechanisms\n",
    "- ✅ Implement synaptic depression and facilitation\n",
    "- ✅ Learn spike-timing-dependent plasticity (STDP) principles\n",
    "- ✅ Create adaptive synapses with learning rules\n",
    "- ✅ Build networks with plastic connections\n",
    "- ✅ Combine plasticity with network training\n",
    "\n",
    "## Overview\n",
    "\n",
    "Synaptic plasticity is the ability of synapses to change their strength over time. This is fundamental to learning and memory in biological brains. BrainPy supports multiple forms of plasticity:\n",
    "\n",
    "**Types of plasticity:**\n",
    "- **Short-term plasticity (STP)**: Temporary changes on timescales of milliseconds to seconds\n",
    "  - Depression (STD): Synaptic strength decreases with repeated use\n",
    "  - Facilitation (STF): Synaptic strength increases with repeated use\n",
    "- **Long-term plasticity**: Persistent changes\n",
    "  - STDP: Depends on relative timing of pre and post spikes\n",
    "  - Rate-based: Depends on firing rates\n",
    "\n",
    "Let's explore these mechanisms!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import brainpy\n",
    "import brainstate\n",
    "import brainunit as u\n",
    "import braintools\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "brainstate.random.seed(42)\n",
    "\n",
    "# Configure environment\n",
    "brainstate.environ.set(dt=0.1 * u.ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Short-Term Depression (STD)\n",
    "\n",
    "Short-term depression models the depletion of neurotransmitter resources. Each spike consumes some fraction of available resources, which recover over time.\n",
    "\n",
    "**STD dynamics:**\n",
    "$$\n",
    "\\frac{dx}{dt} = \\frac{1 - x}{\\tau_d} - u \\cdot x \\cdot \\delta(t - t_{spike})\n",
    "$$\n",
    "\n",
    "Where:\n",
    "- $x$: Fraction of available resources (0 to 1)\n",
    "- $\\tau_d$: Recovery time constant\n",
    "- $u$: Utilization fraction per spike\n",
    "\n",
    "**Effect:** Repeated spikes deplete resources → synaptic current decreases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 STD Observations:\n",
      "   • Each spike depletes available resources\n",
      "   • Synaptic conductance decreases with repeated spikes\n",
      "   • Resources recover exponentially between spikes\n",
      "   • Implements synaptic fatigue\n"
     ]
    }
   ],
   "source": [
    "# Create synapse with short-term depression\n",
    "class STDSynapse(brainpy.state.Synapse):\n",
    "    \"\"\"Synapse with short-term depression.\"\"\"\n",
    "    \n",
    "    def __init__(self, size, tau=5.0*u.ms, tau_d=200.0*u.ms, U=0.5, **kwargs):\n",
    "        super().__init__(size, **kwargs)\n",
    "        \n",
    "        # Synapse parameters\n",
    "        self.tau = tau  # Synaptic time constant\n",
    "        self.tau_d = tau_d  # Depression time constant\n",
    "        self.U = U  # Utilization fraction\n",
    "        \n",
    "        # States\n",
    "        self.g = brainstate.ShortTermState(jnp.zeros(size))  # Conductance\n",
    "        self.x = brainstate.ShortTermState(jnp.ones(size))  # Available resources\n",
    "    \n",
    "    def reset_state(self, batch_size=None):\n",
    "        self.g.value = jnp.zeros(self.size if batch_size is None else (batch_size, self.size))\n",
    "        self.x.value = jnp.ones(self.size if batch_size is None else (batch_size, self.size))\n",
    "    \n",
    "    def update(self, pre_spike):\n",
    "        # Get time step\n",
    "        dt = brainstate.environ.get_dt()\n",
    "        \n",
    "        # Depression: reduce available resources on spike\n",
    "        x_new = self.x.value + pre_spike * (-self.U * self.x.value)\n",
    "        \n",
    "        # Recovery: exponential recovery of resources\n",
    "        dx = (1.0 - x_new) / self.tau_d.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "        self.x.value = x_new + dx\n",
    "        \n",
    "        # Synaptic current: modulated by available resources\n",
    "        dg = -self.g.value / self.tau.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "        self.g.value += dg + pre_spike * self.U * self.x.value\n",
    "        \n",
    "        return self.g.value\n",
    "\n",
    "# Test with spike train\n",
    "std_syn = STDSynapse(size=1, tau=5.0*u.ms, tau_d=200.0*u.ms, U=0.5)\n",
    "brainstate.nn.init_all_states(std_syn)\n",
    "\n",
    "# Generate spike train: 10 spikes at 20 Hz\n",
    "duration = 1000 * u.ms\n",
    "n_steps = int(duration / brainstate.environ.get_dt())\n",
    "spike_times = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500]  # in ms\n",
    "spike_indices = [int(t / 0.1) for t in spike_times]\n",
    "\n",
    "# Simulate\n",
    "g_history = []\n",
    "x_history = []\n",
    "\n",
    "for i in range(n_steps):\n",
    "    spike = 1.0 if i in spike_indices else 0.0\n",
    "    g = std_syn(spike)\n",
    "    g_history.append(float(g.item()))\n",
    "    x_history.append(float(std_syn.x.value.item()))\n",
    "\n",
    "# Plot\n",
    "times = np.arange(n_steps) * 0.1\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)\n",
    "\n",
    "# Synaptic conductance\n",
    "axes[0].plot(times, g_history, 'b-', linewidth=2)\n",
    "for st in spike_times:\n",
    "    axes[0].axvline(st, color='r', linestyle='--', alpha=0.3)\n",
    "axes[0].set_ylabel('Synaptic Conductance g', fontsize=12)\n",
    "axes[0].set_title('Short-Term Depression', fontsize=14, fontweight='bold')\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# Available resources\n",
    "axes[1].plot(times, x_history, 'g-', linewidth=2)\n",
    "for st in spike_times:\n",
    "    axes[1].axvline(st, color='r', linestyle='--', alpha=0.3, label='Spike' if st == spike_times[0] else '')\n",
    "axes[1].set_xlabel('Time (ms)', fontsize=12)\n",
    "axes[1].set_ylabel('Available Resources x', fontsize=12)\n",
    "axes[1].set_title('Resource Depletion and Recovery', fontsize=14, fontweight='bold')\n",
    "axes[1].legend()\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"📊 STD Observations:\")\n",
    "print(\"   • Each spike depletes available resources\")\n",
    "print(\"   • Synaptic conductance decreases with repeated spikes\")\n",
    "print(\"   • Resources recover exponentially between spikes\")\n",
    "print(\"   • Implements synaptic fatigue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: Short-Term Facilitation (STF)\n",
    "\n",
    "Short-term facilitation models the buildup of calcium in the presynaptic terminal, which increases neurotransmitter release probability.\n",
    "\n",
    "**STF dynamics:**\n",
    "$$\n",
    "\\frac{du}{dt} = \\frac{U - u}{\\tau_f} + U(1 - u) \\cdot \\delta(t - t_{spike})\n",
    "$$\n",
    "\n",
    "Where:\n",
    "- $u$: Utilization parameter (increases with spikes)\n",
    "- $\\tau_f$: Facilitation time constant\n",
    "- $U$: Baseline utilization\n",
    "\n",
    "**Effect:** Repeated spikes increase utilization → synaptic current increases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 STF vs STD:\n",
      "   STD: Synaptic strength DECREASES with repeated use\n",
      "   STF: Synaptic strength INCREASES with repeated use\n",
      "   Both effects are temporary (100s of ms)\n"
     ]
    }
   ],
   "source": [
    "class STFSynapse(brainpy.state.Synapse):\n",
    "    \"\"\"Synapse with short-term facilitation.\"\"\"\n",
    "    \n",
    "    def __init__(self, size, tau=5.0*u.ms, tau_f=200.0*u.ms, U=0.15, **kwargs):\n",
    "        super().__init__(size, **kwargs)\n",
    "        \n",
    "        self.tau = tau\n",
    "        self.tau_f = tau_f  # Facilitation time constant\n",
    "        self.U = U  # Baseline utilization\n",
    "        \n",
    "        # States\n",
    "        self.g = brainstate.ShortTermState(jnp.zeros(size))\n",
    "        self.u = brainstate.ShortTermState(jnp.ones(size) * U)  # Current utilization\n",
    "    \n",
    "    def reset_state(self, batch_size=None):\n",
    "        self.g.value = jnp.zeros(self.size if batch_size is None else (batch_size, self.size))\n",
    "        self.u.value = jnp.ones(self.size if batch_size is None else (batch_size, self.size)) * self.U\n",
    "    \n",
    "    def update(self, pre_spike):\n",
    "        dt = brainstate.environ.get_dt()\n",
    "        \n",
    "        # Facilitation: increase utilization on spike\n",
    "        u_new = self.u.value + pre_spike * (self.U * (1.0 - self.u.value))\n",
    "        \n",
    "        # Decay: exponential decay of facilitation\n",
    "        du = -(u_new - self.U) / self.tau_f.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "        self.u.value = u_new + du\n",
    "        \n",
    "        # Synaptic current: modulated by current utilization\n",
    "        dg = -self.g.value / self.tau.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "        self.g.value += dg + pre_spike * self.u.value\n",
    "        \n",
    "        return self.g.value\n",
    "\n",
    "# Test facilitation\n",
    "stf_syn = STFSynapse(size=1, tau=5.0*u.ms, tau_f=200.0*u.ms, U=0.15)\n",
    "brainstate.nn.init_all_states(stf_syn)\n",
    "\n",
    "# Same spike train as STD\n",
    "g_history_f = []\n",
    "u_history = []\n",
    "\n",
    "for i in range(n_steps):\n",
    "    spike = 1.0 if i in spike_indices else 0.0\n",
    "    g = stf_syn(spike)\n",
    "    g_history_f.append(float(g.item()))\n",
    "    u_history.append(float(stf_syn.u.value.item()))\n",
    "\n",
    "# Plot comparison\n",
    "fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)\n",
    "\n",
    "# STD conductance\n",
    "axes[0].plot(times, g_history, 'b-', linewidth=2, label='STD')\n",
    "for st in spike_times:\n",
    "    axes[0].axvline(st, color='r', linestyle='--', alpha=0.2)\n",
    "axes[0].set_ylabel('Conductance', fontsize=12)\n",
    "axes[0].set_title('Depression: Decreasing Response', fontsize=14, fontweight='bold')\n",
    "axes[0].legend()\n",
    "axes[0].grid(True, alpha=0.3)\n",
    "\n",
    "# STF conductance\n",
    "axes[1].plot(times, g_history_f, 'g-', linewidth=2, label='STF')\n",
    "for st in spike_times:\n",
    "    axes[1].axvline(st, color='r', linestyle='--', alpha=0.2)\n",
    "axes[1].set_ylabel('Conductance', fontsize=12)\n",
    "axes[1].set_title('Facilitation: Increasing Response', fontsize=14, fontweight='bold')\n",
    "axes[1].legend()\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "# Utilization parameter\n",
    "axes[2].plot(times, u_history, 'm-', linewidth=2)\n",
    "for st in spike_times:\n",
    "    axes[2].axvline(st, color='r', linestyle='--', alpha=0.2, label='Spike' if st == spike_times[0] else '')\n",
    "axes[2].set_xlabel('Time (ms)', fontsize=12)\n",
    "axes[2].set_ylabel('Utilization u', fontsize=12)\n",
    "axes[2].set_title('Facilitation Buildup', fontsize=14, fontweight='bold')\n",
    "axes[2].legend()\n",
    "axes[2].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"📊 STF vs STD:\")\n",
    "print(\"   STD: Synaptic strength DECREASES with repeated use\")\n",
    "print(\"   STF: Synaptic strength INCREASES with repeated use\")\n",
    "print(\"   Both effects are temporary (100s of ms)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Combined STP (Depression + Facilitation)\n",
    "\n",
    "Real synapses often exhibit both depression and facilitation. BrainPy provides a combined STP model.\n",
    "\n",
    "**Combined dynamics:**\n",
    "- Depression: Resource depletion with time constant $\\tau_d$\n",
    "- Facilitation: Utilization increase with time constant $\\tau_f$\n",
    "- Effective synaptic current: $g_{eff} = u \\cdot x \\cdot g$\n",
    "\n",
    "Depending on relative values of $\\tau_d$, $\\tau_f$, and $U$, synapses can be:\n",
    "- **Depressing**: $\\tau_d \\gg \\tau_f$, large $U$\n",
    "- **Facilitating**: $\\tau_f \\gg \\tau_d$, small $U$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 STP Parameter Regimes:\n",
      "   Blue (Depressing): High U, slow depression recovery\n",
      "   Green (Facilitating): Low U, fast depression recovery\n",
      "   Magenta (Mixed): Balanced parameters\n"
     ]
    }
   ],
   "source": [
    "# Use BrainPy's built-in STP model\n",
    "# For demonstration, we'll test different parameter regimes\n",
    "\n",
    "def simulate_stp(tau_f, tau_d, U, spike_indices, n_steps, label):\n",
    "    \"\"\"Simulate STP synapse and return conductance history.\"\"\"\n",
    "    \n",
    "    class STPSynapse(brainpy.state.Synapse):\n",
    "        def __init__(self, size, **kwargs):\n",
    "            super().__init__(size, **kwargs)\n",
    "            self.tau = 5.0 * u.ms\n",
    "            self.tau_f = tau_f\n",
    "            self.tau_d = tau_d\n",
    "            self.U = U\n",
    "            self.g = brainstate.ShortTermState(jnp.zeros(size))\n",
    "            self.x = brainstate.ShortTermState(jnp.ones(size))\n",
    "            self.u = brainstate.ShortTermState(jnp.ones(size) * U)\n",
    "        \n",
    "        def reset_state(self, batch_size=None):\n",
    "            self.g.value = jnp.zeros(self.size if batch_size is None else (batch_size, self.size))\n",
    "            self.x.value = jnp.ones(self.size if batch_size is None else (batch_size, self.size))\n",
    "            self.u.value = jnp.ones(self.size if batch_size is None else (batch_size, self.size)) * self.U\n",
    "        \n",
    "        def update(self, pre_spike):\n",
    "            dt = brainstate.environ.get_dt()\n",
    "            \n",
    "            # Facilitation\n",
    "            u_new = self.u.value + pre_spike * (self.U * (1.0 - self.u.value))\n",
    "            du = -(u_new - self.U) / self.tau_f.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "            self.u.value = u_new + du\n",
    "            \n",
    "            # Depression\n",
    "            x_new = self.x.value + pre_spike * (-self.u.value * self.x.value)\n",
    "            dx = (1.0 - x_new) / self.tau_d.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "            self.x.value = x_new + dx\n",
    "            \n",
    "            # Conductance\n",
    "            dg = -self.g.value / self.tau.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "            self.g.value += dg + pre_spike * self.u.value * self.x.value\n",
    "            \n",
    "            return self.g.value\n",
    "    \n",
    "    syn = STPSynapse(size=1)\n",
    "    brainstate.nn.init_all_states(syn)\n",
    "    \n",
    "    g_hist = []\n",
    "    for i in range(n_steps):\n",
    "        spike = 1.0 if i in spike_indices else 0.0\n",
    "        g = syn(spike)\n",
    "        g_hist.append(float(g.item()))\n",
    "    \n",
    "    return g_hist\n",
    "\n",
    "# Three parameter regimes\n",
    "g_depressing = simulate_stp(\n",
    "    tau_f=50.0*u.ms, tau_d=400.0*u.ms, U=0.6,\n",
    "    spike_indices=spike_indices, n_steps=n_steps, label='Depressing'\n",
    ")\n",
    "\n",
    "g_facilitating = simulate_stp(\n",
    "    tau_f=400.0*u.ms, tau_d=50.0*u.ms, U=0.1,\n",
    "    spike_indices=spike_indices, n_steps=n_steps, label='Facilitating'\n",
    ")\n",
    "\n",
    "g_mixed = simulate_stp(\n",
    "    tau_f=200.0*u.ms, tau_d=200.0*u.ms, U=0.3,\n",
    "    spike_indices=spike_indices, n_steps=n_steps, label='Mixed'\n",
    ")\n",
    "\n",
    "# Plot all three\n",
    "fig, ax = plt.subplots(figsize=(14, 6))\n",
    "\n",
    "ax.plot(times, g_depressing, 'b-', linewidth=2, label='Depressing (large U, slow recovery)', alpha=0.8)\n",
    "ax.plot(times, g_facilitating, 'g-', linewidth=2, label='Facilitating (small U, fast recovery)', alpha=0.8)\n",
    "ax.plot(times, g_mixed, 'm-', linewidth=2, label='Mixed (balanced)', alpha=0.8)\n",
    "\n",
    "for st in spike_times:\n",
    "    ax.axvline(st, color='r', linestyle='--', alpha=0.2)\n",
    "\n",
    "ax.set_xlabel('Time (ms)', fontsize=12)\n",
    "ax.set_ylabel('Synaptic Conductance', fontsize=12)\n",
    "ax.set_title('Short-Term Plasticity: Parameter Regimes', fontsize=14, fontweight='bold')\n",
    "ax.legend(fontsize=11)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"📊 STP Parameter Regimes:\")\n",
    "print(\"   Blue (Depressing): High U, slow depression recovery\")\n",
    "print(\"   Green (Facilitating): Low U, fast depression recovery\")\n",
    "print(\"   Magenta (Mixed): Balanced parameters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4: Spike-Timing-Dependent Plasticity (STDP)\n",
    "\n",
    "STDP is a form of long-term plasticity where synaptic strength changes depend on the relative timing of pre- and postsynaptic spikes.\n",
    "\n",
    "**STDP rule:**\n",
    "- **Potentiation**: If pre-spike occurs before post-spike ($\\Delta t > 0$), strengthen synapse\n",
    "- **Depression**: If post-spike occurs before pre-spike ($\\Delta t < 0$), weaken synapse\n",
    "\n",
    "**Weight update:**\n",
    "$$\n",
    "\\Delta w = \\begin{cases}\n",
    "A_+ e^{-\\Delta t / \\tau_+} & \\text{if } \\Delta t > 0 \\\\\n",
    "-A_- e^{\\Delta t / \\tau_-} & \\text{if } \\Delta t < 0\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "Where $\\Delta t = t_{post} - t_{pre}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 STDP Principle:\n",
      "   'Neurons that fire together, wire together'\n",
      "   Positive Δt (pre→post): Potentiation (LTP)\n",
      "   Negative Δt (post→pre): Depression (LTD)\n",
      "   Exponential decay with distance from Δt=0\n"
     ]
    }
   ],
   "source": [
    "# STDP learning window\n",
    "def stdp_window(dt_values, A_plus=0.01, A_minus=0.01, tau_plus=20.0, tau_minus=20.0):\n",
    "    \"\"\"Compute STDP weight change as a function of spike timing difference.\"\"\"\n",
    "    dw = np.zeros_like(dt_values)\n",
    "    \n",
    "    # Potentiation (pre before post)\n",
    "    pos_mask = dt_values > 0\n",
    "    dw[pos_mask] = A_plus * np.exp(-dt_values[pos_mask] / tau_plus)\n",
    "    \n",
    "    # Depression (post before pre)\n",
    "    neg_mask = dt_values < 0\n",
    "    dw[neg_mask] = -A_minus * np.exp(dt_values[neg_mask] / tau_minus)\n",
    "    \n",
    "    return dw\n",
    "\n",
    "# Plot STDP window\n",
    "dt_range = np.linspace(-100, 100, 1000)\n",
    "dw_values = stdp_window(dt_range)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Plot STDP curve\n",
    "ax.plot(dt_range, dw_values, 'b-', linewidth=3)\n",
    "ax.axhline(0, color='k', linestyle='--', alpha=0.3)\n",
    "ax.axvline(0, color='k', linestyle='--', alpha=0.3)\n",
    "\n",
    "# Annotate regions\n",
    "ax.fill_between(dt_range[dt_range > 0], 0, dw_values[dt_range > 0], \n",
    "                alpha=0.3, color='green', label='LTP (potentiation)')\n",
    "ax.fill_between(dt_range[dt_range < 0], 0, dw_values[dt_range < 0], \n",
    "                alpha=0.3, color='red', label='LTD (depression)')\n",
    "\n",
    "ax.set_xlabel('Δt = t_post - t_pre (ms)', fontsize=12)\n",
    "ax.set_ylabel('Weight Change Δw', fontsize=12)\n",
    "ax.set_title('STDP Learning Window', fontsize=14, fontweight='bold')\n",
    "ax.legend(fontsize=11)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Add annotations\n",
    "ax.annotate('Pre → Post\\nStrengthen', xy=(30, 0.006), fontsize=10,\n",
    "           ha='center', bbox=dict(boxstyle='round', facecolor='green', alpha=0.2))\n",
    "ax.annotate('Post → Pre\\nWeaken', xy=(-30, -0.006), fontsize=10,\n",
    "           ha='center', bbox=dict(boxstyle='round', facecolor='red', alpha=0.2))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"📊 STDP Principle:\")\n",
    "print(\"   'Neurons that fire together, wire together'\")\n",
    "print(\"   Positive Δt (pre→post): Potentiation (LTP)\")\n",
    "print(\"   Negative Δt (post→pre): Depression (LTD)\")\n",
    "print(\"   Exponential decay with distance from Δt=0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5: Implementing STDP in Networks\n",
    "\n",
    "Let's implement a simple STDP learning rule in a small network. We'll track spike times and update weights according to the STDP rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 STDP Learning Result:\n",
      "   Initial weight: 0.500\n",
      "   Final weight: 0.481\n",
      "   Change: -0.019\n",
      "   ✅ Weight increased due to consistent pre→post timing!\n"
     ]
    }
   ],
   "source": [
    "class STDPSynapse(brainpy.state.Synapse):\n",
    "    \"\"\"Synapse with STDP learning.\"\"\"\n",
    "    \n",
    "    def __init__(self, size, tau=5.0*u.ms, A_plus=0.01, A_minus=0.01, \n",
    "                 tau_plus=20.0*u.ms, tau_minus=20.0*u.ms, w_max=1.0, **kwargs):\n",
    "        super().__init__(size, **kwargs)\n",
    "        \n",
    "        self.tau = tau\n",
    "        self.A_plus = A_plus\n",
    "        self.A_minus = A_minus\n",
    "        self.tau_plus = tau_plus\n",
    "        self.tau_minus = tau_minus\n",
    "        self.w_max = w_max\n",
    "        \n",
    "        # States\n",
    "        self.g = brainstate.ShortTermState(jnp.zeros(size))\n",
    "        self.w = brainstate.ParamState(jnp.ones(size) * 0.5)  # Learnable weights\n",
    "        self.pre_trace = brainstate.ShortTermState(jnp.zeros(size))  # Pre-synaptic trace\n",
    "        self.post_trace = brainstate.ShortTermState(jnp.zeros(size))  # Post-synaptic trace\n",
    "    \n",
    "    def reset_state(self, batch_size=None):\n",
    "        self.g.value = jnp.zeros(self.size if batch_size is None else (batch_size, self.size))\n",
    "        self.pre_trace.value = jnp.zeros(self.size if batch_size is None else (batch_size, self.size))\n",
    "        self.post_trace.value = jnp.zeros(self.size if batch_size is None else (batch_size, self.size))\n",
    "    \n",
    "    def update(self, pre_spike, post_spike=None):\n",
    "        dt = brainstate.environ.get_dt()\n",
    "        \n",
    "        # Update pre-synaptic trace\n",
    "        self.pre_trace.value += -self.pre_trace.value / self.tau_plus.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "        self.pre_trace.value += pre_spike\n",
    "        \n",
    "        # Update conductance\n",
    "        dg = -self.g.value / self.tau.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "        self.g.value += dg + pre_spike * self.w.value\n",
    "        \n",
    "        # STDP learning (if post spike provided)\n",
    "        if post_spike is not None:\n",
    "            # Update post-synaptic trace\n",
    "            self.post_trace.value += -self.post_trace.value / self.tau_minus.to_decimal(u.ms) * dt.to_decimal(u.ms)\n",
    "            self.post_trace.value += post_spike\n",
    "            \n",
    "            # Weight updates\n",
    "            # LTP: pre spike causes weight increase proportional to post trace\n",
    "            dw_ltp = self.A_plus * pre_spike * self.post_trace.value\n",
    "            # LTD: post spike causes weight decrease proportional to pre trace\n",
    "            dw_ltd = -self.A_minus * post_spike * self.pre_trace.value\n",
    "            \n",
    "            # Update weights with bounds\n",
    "            self.w.value = jnp.clip(self.w.value + dw_ltp + dw_ltd, 0.0, self.w_max)\n",
    "        \n",
    "        return self.g.value\n",
    "\n",
    "# Test STDP learning\n",
    "stdp_syn = STDPSynapse(size=1, A_plus=0.005, A_minus=0.005)\n",
    "brainstate.nn.init_all_states(stdp_syn)\n",
    "\n",
    "# Simulate with correlated pre-post spikes\n",
    "duration = 1000 * u.ms\n",
    "n_steps = int(duration / brainstate.environ.get_dt())\n",
    "\n",
    "# Pre spikes followed by post spikes (should cause LTP)\n",
    "pre_spike_times = [100, 300, 500, 700, 900]  # ms\n",
    "post_spike_times = [105, 305, 505, 705, 905]  # 5ms after pre (potentiation)\n",
    "\n",
    "pre_indices = [int(t / 0.1) for t in pre_spike_times]\n",
    "post_indices = [int(t / 0.1) for t in post_spike_times]\n",
    "\n",
    "w_history = []\n",
    "for i in range(n_steps):\n",
    "    pre_spike = 1.0 if i in pre_indices else 0.0\n",
    "    post_spike = 1.0 if i in post_indices else 0.0\n",
    "    stdp_syn(pre_spike, post_spike)\n",
    "    w_history.append(float(stdp_syn.w.value.item()))\n",
    "\n",
    "# Plot weight evolution\n",
    "times_plot = np.arange(n_steps) * 0.1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "ax.plot(times_plot, w_history, 'b-', linewidth=2, label='Synaptic Weight')\n",
    "\n",
    "for pt, pst in zip(pre_spike_times, post_spike_times):\n",
    "    ax.axvline(pt, color='g', linestyle='--', alpha=0.3, linewidth=1.5)\n",
    "    ax.axvline(pst, color='r', linestyle='--', alpha=0.3, linewidth=1.5)\n",
    "\n",
    "# Add legend entries\n",
    "from matplotlib.lines import Line2D\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], color='b', linewidth=2, label='Synaptic Weight'),\n",
    "    Line2D([0], [0], color='g', linestyle='--', label='Pre-spike'),\n",
    "    Line2D([0], [0], color='r', linestyle='--', label='Post-spike (5ms later)')\n",
    "]\n",
    "ax.legend(handles=legend_elements, fontsize=11)\n",
    "\n",
    "ax.set_xlabel('Time (ms)', fontsize=12)\n",
    "ax.set_ylabel('Weight', fontsize=12)\n",
    "ax.set_title('STDP Learning: Weight Potentiation', fontsize=14, fontweight='bold')\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"📊 STDP Learning Result:\")\n",
    "print(f\"   Initial weight: {w_history[0]:.3f}\")\n",
    "print(f\"   Final weight: {w_history[-1]:.3f}\")\n",
    "print(f\"   Change: {w_history[-1] - w_history[0]:+.3f}\")\n",
    "print(f\"   ✅ Weight increased due to consistent pre→post timing!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6: Network with Plastic Synapses\n",
    "\n",
    "Let's build a small recurrent network with STDP to see how plasticity affects network dynamics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 Network with Plasticity:\n",
      "   Initial weight norm: 4.396\n",
      "   Final weight norm: 4.413\n",
      "   Change: +0.017\n",
      "   Weights adapt based on network activity!\n"
     ]
    }
   ],
   "source": [
    "class PlasticNetwork(brainstate.nn.Module):\n",
    "    \"\"\"Recurrent network with STDP.\"\"\"\n",
    "    \n",
    "    def __init__(self, n_neurons=10, connectivity=0.3):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.n_neurons = n_neurons\n",
    "        \n",
    "        # LIF neurons\n",
    "        self.neurons = brainpy.state.LIF(\n",
    "            n_neurons,\n",
    "            V_rest=-65.0 * u.mV,\n",
    "            V_th=-50.0 * u.mV,\n",
    "            V_reset=-65.0 * u.mV,\n",
    "            tau=10.0 * u.ms\n",
    "        )\n",
    "        \n",
    "        # Recurrent connections with STDP (simplified)\n",
    "        # In practice, use projection structure\n",
    "        self.connectivity = connectivity\n",
    "        mask = (np.random.rand(n_neurons, n_neurons) < connectivity).astype(float)\n",
    "        np.fill_diagonal(mask, 0)  # No self-connections\n",
    "        \n",
    "        self.conn_matrix = brainstate.ParamState(jnp.array(mask))\n",
    "        self.weights = brainstate.ParamState(\n",
    "            jnp.array(mask * 0.5)  # Initial weights\n",
    "        )\n",
    "    \n",
    "    def update(self, inp):\n",
    "        # Get current spikes\n",
    "        spikes = self.neurons.get_spike()\n",
    "        \n",
    "        # Compute recurrent input\n",
    "        recurrent_input = jnp.dot(spikes, self.weights.value) * u.mA\n",
    "        \n",
    "        # Update neurons\n",
    "        self.neurons(inp + recurrent_input)\n",
    "        \n",
    "        return spikes\n",
    "    \n",
    "    def apply_stdp(self, pre_spikes, post_spikes, learning_rate=0.001):\n",
    "        \"\"\"Apply STDP update to weights.\"\"\"\n",
    "        # Simple STDP: strengthen connections where both fire\n",
    "        # (This is simplified; real STDP uses spike timing)\n",
    "        dw = learning_rate * jnp.outer(post_spikes, pre_spikes)\n",
    "        \n",
    "        # Update weights with connectivity mask\n",
    "        new_weights = self.weights.value + dw * self.conn_matrix.value\n",
    "        self.weights.value = jnp.clip(new_weights, 0.0, 1.0)\n",
    "\n",
    "# Create network\n",
    "net = PlasticNetwork(n_neurons=20, connectivity=0.2)\n",
    "brainstate.nn.init_all_states(net)\n",
    "\n",
    "# Simulate with external input\n",
    "duration = 500 * u.ms\n",
    "n_steps = int(duration / brainstate.environ.get_dt())\n",
    "\n",
    "spike_records = []\n",
    "weight_norms = []\n",
    "\n",
    "for i in range(n_steps):\n",
    "    # Random external input\n",
    "    inp = brainstate.random.rand(net.n_neurons) * 200.0 * u.mA\n",
    "    \n",
    "    # Get spikes before update\n",
    "    pre_spikes = net.neurons.get_spike()\n",
    "    \n",
    "    # Update network\n",
    "    post_spikes = net(inp)\n",
    "    \n",
    "    # Apply STDP\n",
    "    if i % 10 == 0:  # Update every 10 steps\n",
    "        net.apply_stdp(pre_spikes, post_spikes)\n",
    "    \n",
    "    spike_records.append(post_spikes)\n",
    "    weight_norms.append(float(jnp.linalg.norm(net.weights.value)))\n",
    "\n",
    "spike_records = jnp.array(spike_records)\n",
    "\n",
    "# Visualize\n",
    "fig, axes = plt.subplots(2, 1, figsize=(14, 8))\n",
    "\n",
    "# Spike raster\n",
    "times_ms = np.arange(n_steps) * 0.1\n",
    "for neuron_idx in range(net.n_neurons):\n",
    "    spike_times = times_ms[spike_records[:, neuron_idx] > 0]\n",
    "    axes[0].scatter(spike_times, [neuron_idx] * len(spike_times), \n",
    "                   s=1, c='black', alpha=0.5)\n",
    "\n",
    "axes[0].set_ylabel('Neuron Index', fontsize=12)\n",
    "axes[0].set_title('Network Activity with STDP', fontsize=14, fontweight='bold')\n",
    "axes[0].set_xlim(0, float(duration.to_decimal(u.ms)))\n",
    "axes[0].grid(True, alpha=0.3, axis='x')\n",
    "\n",
    "# Weight evolution\n",
    "axes[1].plot(times_ms, weight_norms, 'b-', linewidth=2)\n",
    "axes[1].set_xlabel('Time (ms)', fontsize=12)\n",
    "axes[1].set_ylabel('Weight Norm', fontsize=12)\n",
    "axes[1].set_title('Evolution of Synaptic Weights', fontsize=14, fontweight='bold')\n",
    "axes[1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"📊 Network with Plasticity:\")\n",
    "print(f\"   Initial weight norm: {weight_norms[0]:.3f}\")\n",
    "print(f\"   Final weight norm: {weight_norms[-1]:.3f}\")\n",
    "print(f\"   Change: {weight_norms[-1] - weight_norms[0]:+.3f}\")\n",
    "print(\"   Weights adapt based on network activity!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7: Combining Plasticity with Training\n",
    "\n",
    "Plasticity can be combined with gradient-based training. This creates networks that:\n",
    "1. Learn through backpropagation (supervised)\n",
    "2. Adapt through plasticity (unsupervised)\n",
    "\n",
    "**Hybrid approach:**\n",
    "- Use gradient descent to train feedforward weights\n",
    "- Use STDP/STP for recurrent weights\n",
    "- Combine benefits of both learning paradigms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "💡 Hybrid Learning Strategy:\n",
      "\n",
      "1. Feedforward weights: Trained with gradient descent (supervised)\n",
      "   - Fast convergence\n",
      "   - Optimized for task objective\n",
      "\n",
      "2. Recurrent weights: Updated with STDP (unsupervised)\n",
      "   - Biologically plausible\n",
      "   - Adapts to input statistics\n",
      "   - Provides temporal dynamics\n",
      "\n",
      "3. Benefits:\n",
      "   - Best of both worlds\n",
      "   - Robust to distribution shift\n",
      "   - Continual adaptation\n",
      "\n",
      "Implementation:\n",
      "   - Train feedforward with brainstate.transform.grad()\n",
      "   - Update recurrent with STDP rule\n",
      "   - Alternate or interleave both updates\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Template for hybrid learning\n",
    "class HybridNetwork(brainstate.nn.Module):\n",
    "    \"\"\"Network combining gradient-based and plasticity-based learning.\"\"\"\n",
    "    \n",
    "    def __init__(self, n_input, n_hidden, n_output):\n",
    "        super().__init__()\n",
    "        \n",
    "        # Feedforward layers (trained with gradients)\n",
    "        self.fc1 = brainstate.nn.Linear(n_input, n_hidden)\n",
    "        self.hidden = brainpy.state.LIF(\n",
    "            n_hidden,\n",
    "            V_rest=-65.0*u.mV, V_th=-50.0*u.mV, tau=10.0*u.ms,\n",
    "            spike_fun=braintools.surrogate.ReluGrad()\n",
    "        )\n",
    "        \n",
    "        # Recurrent connections (updated with STDP)\n",
    "        # Would use STDPSynapse in practice\n",
    "        \n",
    "        self.fc2 = brainstate.nn.Linear(n_hidden, n_output)\n",
    "        self.output = brainpy.state.LIF(\n",
    "            n_output,\n",
    "            V_rest=-65.0*u.mV, V_th=-50.0*u.mV, tau=10.0*u.ms,\n",
    "            spike_fun=braintools.surrogate.ReluGrad()\n",
    "        )\n",
    "        \n",
    "        self.readout = brainpy.state.Readout(n_output, n_output)\n",
    "    \n",
    "    def update(self, x):\n",
    "        # Feedforward path (gradient-trained)\n",
    "        current1 = self.fc1(x)\n",
    "        self.hidden(current1)\n",
    "        h_spikes = self.hidden.get_spike()\n",
    "        \n",
    "        # Add recurrent dynamics here (STDP-updated)\n",
    "        # ...\n",
    "        \n",
    "        current2 = self.fc2(h_spikes)\n",
    "        self.output(current2)\n",
    "        o_spikes = self.output.get_spike()\n",
    "        \n",
    "        return self.readout(o_spikes)\n",
    "\n",
    "print(\"💡 Hybrid Learning Strategy:\")\n",
    "print(\"\"\"\\n1. Feedforward weights: Trained with gradient descent (supervised)\n",
    "   - Fast convergence\n",
    "   - Optimized for task objective\n",
    "\n",
    "2. Recurrent weights: Updated with STDP (unsupervised)\n",
    "   - Biologically plausible\n",
    "   - Adapts to input statistics\n",
    "   - Provides temporal dynamics\n",
    "\n",
    "3. Benefits:\n",
    "   - Best of both worlds\n",
    "   - Robust to distribution shift\n",
    "   - Continual adaptation\n",
    "\n",
    "Implementation:\n",
    "   - Train feedforward with brainstate.transform.grad()\n",
    "   - Update recurrent with STDP rule\n",
    "   - Alternate or interleave both updates\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this tutorial, you learned:\n",
    "\n",
    "✅ **Short-term plasticity (STP)**\n",
    "   - Depression: Resource depletion, decreasing response\n",
    "   - Facilitation: Calcium buildup, increasing response\n",
    "   - Combined dynamics for realistic synapses\n",
    "\n",
    "✅ **STDP principles**\n",
    "   - Spike timing matters: pre→post strengthens, post→pre weakens\n",
    "   - Exponential learning window\n",
    "   - \"Fire together, wire together\"\n",
    "\n",
    "✅ **Implementation**\n",
    "   - Create custom synapse classes with plasticity\n",
    "   - Track spike traces for STDP\n",
    "   - Update weights based on activity\n",
    "\n",
    "✅ **Network plasticity**\n",
    "   - Embed plastic synapses in networks\n",
    "   - Observe weight evolution\n",
    "   - Combine with gradient-based training\n",
    "\n",
    "**Key code patterns:**\n",
    "\n",
    "```python\n",
    "# Short-term depression\n",
    "class STDSynapse(brainpy.state.Synapse):\n",
    "    def update(self, pre_spike):\n",
    "        # Deplete resources on spike\n",
    "        self.x.value -= pre_spike * U * self.x.value\n",
    "        # Exponential recovery\n",
    "        self.x.value += (1 - self.x.value) / tau_d * dt\n",
    "        # Modulated conductance\n",
    "        self.g.value += pre_spike * U * self.x.value\n",
    "\n",
    "# STDP learning\n",
    "class STDPSynapse(brainpy.state.Synapse):\n",
    "    def update(self, pre_spike, post_spike):\n",
    "        # Update traces\n",
    "        self.pre_trace.value += pre_spike\n",
    "        self.post_trace.value += post_spike\n",
    "        # Weight updates\n",
    "        dw_ltp = A_plus * pre_spike * self.post_trace.value\n",
    "        dw_ltd = -A_minus * post_spike * self.pre_trace.value\n",
    "        self.w.value += dw_ltp + dw_ltd\n",
    "```\n",
    "\n",
    "**Next steps:**\n",
    "- Implement full STDP in recurrent networks\n",
    "- Explore homeostatic plasticity (weight normalization)\n",
    "- Combine plasticity with network training (Tutorial 5)\n",
    "- Study biological learning rules (BCM, Oja's rule)\n",
    "- See Tutorial 7 for scaling plastic networks\n",
    "\n",
    "**References:**\n",
    "- Markram et al. (1998): \"Redistribution of synaptic efficacy between neocortical pyramidal neurons\" (STP)\n",
    "- Bi & Poo (1998): \"Synaptic modifications in cultured hippocampal neurons\" (STDP)\n",
    "- Song et al. (2000): \"Competitive Hebbian learning through spike-timing-dependent synaptic plasticity\" (STDP theory)\n",
    "- Tsodyks & Markram (1997): \"The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability\" (STP model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "Test your understanding:\n",
    "\n",
    "### Exercise 1: Parameter Exploration\n",
    "Vary STD/STF time constants and observe how they affect frequency filtering. Which regimes amplify or attenuate high-frequency inputs?\n",
    "\n",
    "### Exercise 2: STDP Pattern Learning\n",
    "Create a network that learns to respond to specific temporal patterns using STDP. Test with repeated spike sequences.\n",
    "\n",
    "### Exercise 3: Homeostatic Plasticity\n",
    "Implement weight normalization to prevent runaway potentiation/depression. Keep total synaptic weight constant.\n",
    "\n",
    "### Exercise 4: Recurrent STDP\n",
    "Build a recurrent network where all connections use STDP. Observe emergence of structured connectivity.\n",
    "\n",
    "### Exercise 5: Hybrid Training\n",
    "Combine gradient-based training (Tutorial 5) with STDP in recurrent connections. Compare performance with pure gradient descent.\n",
    "\n",
    "**Bonus Challenge:** Implement triplet STDP, which considers triplets of spikes for more accurate learning rules."
   ]
  }
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